SKU: 43533426837

Indian Buddha Life Story Hand Carved Fine Décor Statue Gift Idol Showpiece 11"

Sale price$4837.50 Regular price$5375.00
Save 10%

Pay in installments of $1343.75 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Aug 23 - Aug 28

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

Indian Buddha Life Story Hand Carved Fine Décor Statue Gift Idol Showpiece 11"Product Details : Product Code 459956 Width x Depth (Inches) 9 x 5 Inches Height (Inches) 11 Inches Weight (In Kg) 5 Kg Material Brass Made In India USAGE: To decorate living room, pooja room, foyuer, altar, meditation room or entrance to your home or office. And can be placed on top of table or can be installed or placed anywhere indoor outdoor home garden. POSITIVE IMPACT: Founder of World Religion of Buddhism. Creates peaceful ambience filled with

  • Product Details : Product Code - 459956 || Width x Depth (Inches) - 9 x 5 Inches || Height (Inches) - 11 Inches || Weight (In Kg) - 5 Kg || Material - Brass || Made In India
  • USAGE: To decorate living room, pooja room, foyuer, altar, meditation room or entrance to your home or office. And can be placed on top of table or can be installed or placed anywhere indoor/outdoor/home/garden.
  • POSITIVE IMPACT: Founder of World Religion of Buddhism. Creates peaceful ambience filled with harmony, spirituality and positive energy around us. And at the same time acts as magnificient piece of art and collectible.
  • GIFTING: An ideal gifting option to gift someone as a Wedding Gift/Anniversary Gift/Corporate Gift/Return Gift/Birthday Gift/Diwali Gift/Lucky Gift and a gift that can be given on all occassions like private parties or public community events.
  • Ships directly from our warehouse in Delhi. Prices in INR include cost of the product, applicable GST, packing charges, door delivery to your place in India.

Buddha, popular as the Enlightened one, was the philosopher, spiritual teacher, meditator, protector, mentor and the founder of Buddhism on earth. He is the one who was not born as a god but as an ordinary person, named Siddhartha. He got his name owing to his several years of meditation and the life of asceticism which eventually awakened him. His entire life story is the store flooded with many inspiring events that became the part of his journey towards renunciation.

According to the history, Buddha was born into a Sakya clan somewhere in North India, Lumbini towards southern borders of Nepal. He spent his childhood in Kapilavastu. Buddha was born to Suddhodana and Maya, and initially named Siddhartha Gautam. Suddhodana was the king of the local Sakya tribe where Siddhartha was the prince surrounded by all materialistic pleasures of life.

As we dive back into the life of Buddha, we would see that strong predictions about his life were already made by the priests. As per the common ritual, after the birth of the child a ceremony was kept to announce the name of the child. Suddhodana invited Brahmin priests for the ceremony during which one of the priests declared that he would become Buddha in future and renounce the world. After hearing such words from the priest, Suddhodana became careful and surrounded Siddhartha with all forms of luxuries.

He was always kept under the shadow by one man in his service. The best of food, clothes and things were served to him. The palace was well equipped with all sources of best entertainment from music to dance by prominent artists. Suddhodana assured that Siddhartha was kept away even from the thought of misery in every possible way.

At the age of sixteen Buddha was married to Yasodhara, who gave birth to his son named Rahul. His first encounter with misery was at the age of 29 when he went out of the palace in his chariot. Four events took place which transformed the life of Siddhartha. He saw an old age, a diseased body, a corpse and an ascetic. This was the time when his journey as Buddha began and he left the palace in search of a solution to all miseries.

After attaining Enlightenment Buddha started preaching others and spreading spiritual knowledge. His images as an ascetic sitting under Bodhi Tree with different postures of hands like blessing, teaching and touching the earth are very common as a symbol of peace and purity.

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 43533426837

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.2 ★★★★★
Based on 12 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
P
Verified Purchase
Par
Carnegie, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Fort Morgan, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Alexandria, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Dallas, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Alexandria, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

recommand products